Technologies like artificial intelligence and quantum computing are reshaping the boundaries of innovation – each following its own trajectory, yet increasingly interconnected.
On one hand, artificial intelligence is the leading innovation trend across industries; on the other, quantum computing is a rapidly advancing field, with real-world applications beginning to emerge across various domains. It is precisely from their convergence—driven by the need to solve increasingly complex problems in record time—that organizations can unlock the full potential of the quantum paradigm.
The synergy between AI and quantum computing creates opportunities that are simply out of reach for classical technologies.
In this article, we explore the growing synergy between AI and quantum computing, examining the key technological challenges, the most promising areas of application, and the outlook for future development.
At the center of this evolution is the experience of QuantumNet, an Italian startup born as a spin-off of NetCom in collaboration with University of Naples Federico II, committed to bringing quantum technologies out of the lab and into enterprise environments.
What is quantum AI? A rapidly evolving alliance
Today, quantum computing is being explored primarily in two key directions: security—for example, through Quantum Key Distribution (QKD), which leverages the fundamental principles of quantum physics to make cryptographic key transmission truly unbreakable—and applications in optimization and classification, where the trajectory of quantum intersects directly with artificial intelligence.
Development pathways of quantum and AI
Many of the problems AI deals with daily – from image classification to pattern prediction and complex optimization – are now being reimagined through quantum approaches, with very promising results.
Quantum computers, leveraging the principle of superposition, can simultaneously explore multiple solutions, delivering a significant speed-up over classical algorithms for high-dimensional problems.
Hybrid quantum-classical models, which allow AI algorithms to leverage quantum properties such as qubit superposition and entanglement (“AI qubit”), are opening up new horizons for complex problem solving.
This shift requires a significant paradigm change- not only in algorithms but also in programming languages and computing architectures. This is where key quantum AI research areas emerge, such as quantum neural networks and quantum machine learning algorithms. Many machine learning problems boil down to solving linear algebra in high-dimensional vector spaces – tasks for which quantum computing offers substantial advantages.
Looking ahead, the integration of classical and quantum infrastructures—thanks also to the continuous evolution of high-performance computing (HPC) systems—will represent the main pathway forward. Hybrid approaches, where algorithms are designed to harness the strengths of both classical and quantum computers, are set to remain at the core of technological development for years to come.
Quantum vs. Classical approach: a complementary relationship
In advanced artificial intelligence, selecting the most suitable technology—quantum, classical, or a combination—always requires a careful evaluation of the specific characteristics of the problem at hand.
In some cases, the quantum approach enables scenarios that are simply not feasible for classical systems, or would otherwise require unsustainable time and resources. In such instances, quantum delivers truly superior performance. In others, however, the same problems can be solved more efficiently with high-performance classical infrastructure.
This speaks to one of the most common misconceptions: thinking of quantum computing as a universal replacement for traditional computing. In reality, it’s a highly selective accelerator – not an evolutionary step in classical computing, but a separate paradigm that will coexist and integrate with it.
Quantum and AI challenges: from skills to hardware
The convergence of AI and quantum computing is one of the most promising frontiers in tech innovation. But as with any frontier, it comes with major challenges that must be addressed with long-term strategic vision.
1. The skills gap
The first hurdle is talent. Quantum logic breaks the classical rules of computing and programming – no longer based on ones and zeros, but on state superposition, entanglement, and complex transformations.
As with any emerging field, the lack of professionals capable of operating across quantum physics, AI, and software engineering is a tangible barrier.
To address this, QuantumNet – one of Italy’s first quantum-focused startups – has launched the Quantum Computing Academy: a 400-hour intensive training program combining theoretical lessons with real-world industrial projects developed in partnership with large enterprises. This program is now in its 2nd edition and ready for its third edition this autumn.
It’s the first structured initiative in Italy aimed at equipping STEM students with the skills needed to apply quantum principles in enterprise settings.
2. Evolving hardware
Quantum infrastructure is still evolving, and there is currently no computer capable of supporting all possible quantum applications at scale and with stability.
Today’s machines are limited by noise, instability, and a low number of qubits, often requiring highly controlled environments to operate.Still, development is progressing toward more scalable architectures, with real-world applications in sight.
A noteworthy example is the Campania region in Italy – home to both NetCom and QuantumNet – which is emerging as the Italian Quantum Valley. Here, top-tier universities, a vibrant research ecosystem, and quantum computer installations in Naples and Salerno form a unique innovation hub. The future of quantum infrastructure and its industrialization will depend heavily on such ecosystems.
3. The power of the ecosystem
Quantum is not a technology that can evolve in isolation—also because it requires extremely high investments for infrastructure, advanced refrigeration systems, and highly specialized technology. Today, these costs are a significant barrier, meaning that quantum computing remains within the reach of only a few major players worldwide.
This makes strong collaboration between research, industry, and institutions absolutely essential—not only to overcome scientific barriers and translate algorithms into operational solutions, but also to open up opportunities to startups and small businesses, which have enormous innovative potential yet would struggle to access these resources on their own.
QuantumNet has embraced this principle at its core, actively engaging in joint projects and frontier labs with universities, and participating in system-wide initiatives such as Borgo 4.0, a smart city project where the company developed quantum algorithms for optimizing vehicle flows.
Where quantum and AI can make a real difference
The combination of AI and quantum computing lends itself to a wide range of applications, especially in domains where system complexity, data volume, and required response times surpass the limits of classical infrastructure. Here are some high-impact use cases.
1. Smart urban mobility
Real-time traffic regulation is a major challenge for smart cities. Hybrid metaheuristic algorithms can help optimize traffic light timing, manage intersections, and coordinate vehicle-infrastructure interactions predictively – improving flow and reducing congestion and wait times.
2. Early anomaly detection in industrial systems
Quantum classification models, especially variational methods, can enhance anomaly detection systems in sectors like industrial production or telecom networks.
3. Optimizing accessibility and navigation in physical spaces
Quantum algorithms such as QAOA (Quantum Approximate Optimization Algorithm) are well-suited for solving complex routing and pathfinding problems.
Integrated into mobile apps, these models support smart path planning to improve accessibility, reduce transit times, and enhance user experience in high-traffic environments.
4. Quantum-powered deep learning for image recognition
Combining deep learning with quantum computing boosts performance in complex image recognition tasks – especially in high-variability settings. Hybrid quantum-classical models unlock new possibilities for automated visual classification, with real-world impact in sectors such as agri-food, environmental monitoring, and manufacturing.
NetCom and QuantumNet: a model of integrated innovation
Founded in 2021 through collaboration between NetCom and researchers from the University of Naples, QuantumNet is one of Italy’s first startups dedicated entirely to quantum computing. Its mission is bridging the gap between scientific research and real-world applications.
What sets QuantumNet apart is its ability to operate at the intersection of academic research and industrial innovation, leveraging partnerships with universities and the broader synergy with the NetCom Group. This allows it to integrate multidisciplinary expertise and engineering know-how, unlocking new ways to tackle complex challenges such as digital accessibility within the Public Sector and using AI for software development.
Key sectors and application areas include telecommunications, automotive, media, finance, public administration, and defense, all marked by high operational complexity and the need to process large volumes of data in very short timeframes. In these domains, QuantumNet delivers advanced solutions in optimization, classification, and data analysis, tapping into the unique power of the quantum paradigm.
